Optimizing Strategies for Improving Mental Health in Victoria, Australia during the COVID-19 Era: A System Dynamics Modelling Study.

The ongoing COVID-19 pandemic has impacted the mental health of populations and highlighted the limitations of mental health care systems. As the trajectory of the pandemic and the economic recovery are still uncertain, decision tools are needed to help evaluate the best interventions to improve mental health outcomes. We developed a system dynamics model that captures causal relationships among population, demographics, post-secondary education, health services, COVID-19 impact, and mental health outcomes. The study was conducted in the Australian state of Victoria. The model was calibrated using historical data and was stratified by age group and by geographic remoteness. Findings demonstrate that the most effective intervention combination includes economic, social, and health sector initiatives. Assertive post-suicide attempt care is the most impactful health sector intervention, but delaying implementation reduces the potency of its impact. Some evidence-based interventions, such as population-wide community awareness campaigns, are projected to worsen mental health outcomes when implemented on their own. Systems modelling offers a powerful decision-support tool to test alternative strategies for improving mental health outcomes in the Victorian context.

Which Social, Economic, and Health Sector Strategies Will Deliver the Greatest Impacts for Youth Mental Health and Suicide Prevention? Protocol for an Advanced, Systems Modelling Approach.

Background: Current global challenges are generating extensive social disruption and uncertainty that have the potential to undermine the mental health, wellbeing, and futures of young people. The scale and complexity of challenges call for engagement with systems science-based decision analytic tools that can capture the dynamics and interrelationships between physical, social, economic, and health systems, and support effective national and regional responses. At the outset of the pandemic mental health-related systems models were developed for the Australian context, however, the extent to which findings are generalisable across diverse regions remains unknown. This study aims to explore the context dependency of systems modelling insights. Methods: This study will employ a comparative case study design, applying participatory system dynamics modelling across eight diverse regions of Australia to answer three primary research questions: (i) Will current regional differences in key youth mental health outcomes be exacerbated in forward projections due to the social and economic impacts of COVID-19?; (ii) What combination of social policies and health system strengthening initiatives will deliver the greatest impacts within each region?; (iii) To what extent are optimal strategic responses consistent across the diverse regions? We provide a detailed technical blueprint as a potential springboard for more timely construction and deployment of systems models in international contexts to facilitate a broader examination of the question of generalisability and inform investments in the mental health and wellbeing of young people in the post COVID-19 recovery. Discussion: Computer simulation is known as the third pillar of science (after theory and experiment). Simulation allows researchers and decision makers to move beyond what can be manipulated within the scale, time, and ethical limits of the experimental approach. Such learning when achieved collectively, has the potential to enhance regional self-determination, help move beyond incremental adjustments to the status quo, and catalyze transformational change. This research seeks to advance efforts to establish regional decision support infrastructure and empower communities to effectively respond. In addition, this research seeks to move towards an understanding of the extent to which systems modelling insights may be relevant to the global mental health response by encouraging researchers to use, challenge, and advance the existing work for scientific and societal progress.

Accessible detection of SARS-CoV-2 through molecular nanostructures and automated microfluidics.

Accurate and accessible nucleic acid diagnostics is critical to reducing the spread of COVID-19 and resuming socioeconomic activities. Here, we present an integrated platform for the direct detection of SARS-CoV-2 RNA targets near patients. Termed electrochemical system integrating reconfigurable enzyme-DNA nanostructures (eSIREN), the technology leverages responsive molecular nanostructures and automated microfluidics to seamlessly transduce target-induced molecular activation into an enhanced electrochemical signal. Through responsive enzyme-DNA nanostructures, the technology establishes a molecular circuitry that directly recognizes specific RNA targets and catalytically enhances signaling; only upon target hybridization, the molecular nanostructures activate to liberate strong enzymatic activity and initiate cascading reactions. Through automated microfluidics, the system coordinates and interfaces the molecular circuitry with embedded electronics; its pressure actuation and liquid-guiding structures improve not only analytical performance but also automated implementation. The developed platform establishes a detection limit of 7 copies of RNA target per µl, operates against the complex biological background of native patient samples, and is completed in <20 min at room temperature. When clinically evaluated, the technology demonstrates accurate detection in extracted RNA samples and direct swab lysates to diagnose COVID-19 patients.

Collaborative Equilibrium Coupling of Catalytic DNA Nanostructures Enables Programmable Detection of SARS-CoV-2.

Accessible and adaptable nucleic acid diagnostics remains a critical challenge in managing the evolving COVID-19 pandemic. Here, an integrated molecular nanotechnology that enables direct and programmable detection of SARS-CoV-2 RNA targets in native patient specimens is reported. Termed synergistic coupling of responsive equilibrium in enzymatic network (SCREEN), the technology leverages tunable, catalytic molecular nanostructures to establish an interconnected, collaborative architecture. SCREEN mimics the extraordinary organization and functionality of cellular signaling cascades. Through programmable enzyme-DNA nanostructures, SCREEN activates upon interaction with different RNA targets to initiate multi-enzyme catalysis; through system-wide favorable equilibrium shifting, SCREEN directly transduces a single target binding into an amplified electrical signal. To establish collaborative equilibrium coupling in the architecture, a computational model that simulates all reactions to predict overall performance and optimize assay configuration is developed. The developed platform achieves direct and sensitive RNA detection (approaching single-copy detection), fast response (assay reaction is completed within 30 min at room temperature), and robust programmability (across different genetic loci of SARS-CoV-2). When clinically evaluated, the technology demonstrates robust and direct detection in clinical swab lysates to accurately diagnose COVID-19 patients.

Catalytic amplification by transition-state molecular switches for direct and sensitive detection of SARS-CoV-2.

Despite the importance of nucleic acid testing in managing the COVID-19 pandemic, current detection approaches remain limited due to their high complexity and extensive processing. Here, we describe a molecular nanotechnology that enables direct and sensitive detection of viral RNA targets in native clinical samples. The technology, termed catalytic amplification by transition-state molecular switch (CATCH), leverages DNA-enzyme hybrid complexes to form a molecular switch. By ratiometric tuning of its constituents, the multicomponent molecular switch is prepared in a hyperresponsive state-the transition state-that can be readily activated upon the binding of sparse RNA targets to turn on substantial enzymatic activity. CATCH thus achieves superior performance (~8 RNA copies/µl), direct fluorescence detection that bypasses all steps of PCR (<1 hour at room temperature), and versatile implementation (high-throughput 96-well format and portable microfluidic assay). When applied for clinical COVID-19 diagnostics, CATCH demonstrated direct and accurate detection in minimally processed patient swab samples.